Large-scale machine learning, or predictive analytics, is having a powerful impact across many industries. By using machine learning, companies, governments, and not-for-profits are replacing guesses and seat-of-the-pants estimates with valuable data-driven predictions.

Deriving value from machine learning, however, is often impeded by complex technology deployments and long model-development cycles. Fortunately, machine learning and data science are undergoing democratization. Workflow environments make tools for building and evaluating sophisticated machine learning models accessible to a wider range of users. Cloud-based environments provide secure ubiquitous access to data storage and powerful data science tools.

To get you started creating and evaluating your own machine learning models, O’Reilly has commissioned a new report: “Data Science in the Cloud, with Azure Machine Learning and R.” We use an in-depth data science example — predicting bicycle rental demand — to show you how to perform basic data science tasks, including data management, data transformation, machine learning, and model evaluation in the Microsoft Azure Machine Learning cloud environment. Using a free-tier Azure ML account, example R scripts, and the data provided, the report provides hands-on experience with this practical data science example.

Specifically, this report shows you how to complete the following tasks using Azure ML and R:

Stephen F. Elston, Managing Director of Quantia Analytics, LLC is a big data geek and data scientist, with over two decades of experience with predictive analytics, machine learning, and R and S/SPLUS. He leads architecture, development, sales and support for predictive analytics and machine learning solutions. Steve started using S, the predecessor of R, in the mid 1980’s. Steve led R&D for the SPLUS companies, pioneers in introducing the S language into the market. He is a cofounder of FinAnalytica, Inc. Steve holds a PhD degree in Geop...